def check_hours(tickers, tz_exch, tz_loc=DEFAULT_TZ):
"""
Check exchange hours for tickers
Args:
tickers: list of tickers
tz_exch: exchange timezone
tz_loc: local timezone
Returns:
Local and exchange hours
"""
cols = ['Trading_Day_Start_Time_EOD', 'Trading_Day_End_Time_EOD']
con, _ = create_connection()
hours = con.ref(tickers=tickers, flds=cols)
cur_dt = pd.Timestamp('today').strftime('%Y-%m-%d ')
hours.loc[:, 'local'] = hours.value.astype(str).str[:-3]
hours.loc[:, 'exch'] = pd.DatetimeIndex(
cur_dt + hours.value.astype(str)
).tz_localize(tz_loc).tz_convert(tz_exch).strftime('%H:%M')
hours = pd.concat([
hours.set_index(['ticker', 'field']).exch.unstack().loc[:, cols],
hours.set_index(['ticker', 'field']).local.unstack().loc[:, cols],
], axis=1)
hours.columns = ['Exch_Start', 'Exch_End', 'Local_Start', 'Local_End']
return hours
def bdp(tickers, flds, cache=False, **kwargs):
"""
Get reference data and save to
Args:
tickers: tickers
flds: fields to query
cache: bool - use cache to store data
**kwargs: overrides
Returns:
pd.DataFrame
Examples:
>>> bdp('IQ US Equity', 'Crncy', raw=True)
ticker field value
0 IQ US Equity Crncy USD
>>> bdp('IQ US Equity', 'Crncy').reset_index()
ticker crncy
0 IQ US Equity USD
"""
logger = logs.get_logger(bdp, level=kwargs.pop('log', logs.LOG_LEVEL))
con, _ = create_connection()
ovrds = assist.proc_ovrds(**kwargs)
logger.info(
f'loading reference data from Bloomberg:\n'
f'{assist.info_qry(tickers=tickers, flds=flds)}'
)
data = con.ref(tickers=tickers, flds=flds, ovrds=ovrds)
if not cache: return [data]
qry_data = []
for r, snap in data.iterrows():
subset = [r]
data_file = storage.ref_file(
ticker=snap.ticker, fld=snap.field, ext='pkl', cache=cache, **kwargs
)
if data_file:
if not files.exists(data_file): qry_data.append(data.iloc[subset])
files.create_folder(data_file, is_file=True)
data.iloc[subset].to_pickle(data_file)
return qry_data
def bds(tickers, flds, cache=False, **kwargs):
"""
Download block data from Bloomberg
Args:
tickers: ticker(s)
flds: field(s)
cache: whether read from cache
**kwargs: other overrides for query
-> raw: raw output from `pdbdp` library, default False
Returns:
pd.DataFrame: block data
Examples:
>>> import os
>>>
>>> pd.options.display.width = 120
>>> s_dt, e_dt = '20180301', '20181031'
>>> dvd = bds(
... 'NVDA US Equity', 'DVD_Hist_All',
... DVD_Start_Dt=s_dt, DVD_End_Dt=e_dt, raw=True,
... )
>>> dvd.loc[:, ['ticker', 'name', 'value']].head(8)
ticker name value
0 NVDA US Equity Declared Date 2018-08-16
1 NVDA US Equity Ex-Date 2018-08-29
2 NVDA US Equity Record Date 2018-08-30
3 NVDA US Equity Payable Date 2018-09-21
4 NVDA US Equity Dividend Amount 0.15
5 NVDA US Equity Dividend Frequency Quarter
6 NVDA US Equity Dividend Type Regular Cash
7 NVDA US Equity Declared Date 2018-05-10
>>> dvd = bds(
... 'NVDA US Equity', 'DVD_Hist_All',
... DVD_Start_Dt=s_dt, DVD_End_Dt=e_dt,
... )
>>> dvd.reset_index().loc[:, ['ticker', 'ex_date', 'dividend_amount']]
ticker ex_date dividend_amount
0 NVDA US Equity 2018-08-29 0.15
1 NVDA US Equity 2018-05-23 0.15
>>> if not os.environ.get('BBG_ROOT', ''):
... os.environ['BBG_ROOT'] = f'{files.abspath(__file__, 1)}/tests/data'
>>> idx_kw = dict(End_Dt='20181220', cache=True)
>>> idx_wt = bds('DJI Index', 'Indx_MWeight_Hist', **idx_kw)
>>> idx_wt.round(2).tail().reset_index(drop=True)
index_member percent_weight
0 V UN 3.82
1 VZ UN 1.63
2 WBA UW 2.06
3 WMT UN 2.59
4 XOM UN 2.04
>>> idx_wt = bds('DJI Index', 'Indx_MWeight_Hist', **idx_kw)
>>> idx_wt.round(2).head().reset_index(drop=True)
index_member percent_weight
0 AAPL UW 4.65
1 AXP UN 2.84
2 BA UN 9.29
3 CAT UN 3.61
4 CSCO UW 1.26
"""
logger = logs.get_logger(bds, level=kwargs.pop('log', logs.LOG_LEVEL))
has_date = kwargs.pop('has_date', True)
con, _ = create_connection()
ovrds = assist.proc_ovrds(**kwargs)
logger.info(
f'loading block data from Bloomberg:\n'
f'{assist.info_qry(tickers=tickers, flds=flds)}'
)
data = con.bulkref(tickers=tickers, flds=flds, ovrds=ovrds)
if not cache: return [data]
qry_data = []
for (ticker, fld), grp in data.groupby(['ticker', 'field']):
data_file = storage.ref_file(
ticker=ticker, fld=fld, has_date=has_date, ext='pkl', cache=cache, **kwargs
)
if data_file:
if not files.exists(data_file): qry_data.append(grp)
files.create_folder(data_file, is_file=True)
grp.reset_index(drop=True).to_pickle(data_file)
return qry_data
def bdib(ticker, dt, typ='TRADE', batch=False, log=logs.LOG_LEVEL) -> pd.DataFrame:
"""
Download intraday data and save to cache
Args:
ticker: ticker name
dt: date to download
typ: [TRADE, BID, ASK, BID_BEST, ASK_BEST, BEST_BID, BEST_ASK]
batch: whether is batch process to download data
log: level of logs
Returns:
pd.DataFrame
"""
from xbbg.core import missing
logger = logs.get_logger(bdib, level=log)
t_1 = pd.Timestamp('today').date() - pd.Timedelta('1D')
whole_day = pd.Timestamp(dt).date() < t_1
if (not whole_day) and batch:
logger.warning(f'querying date {t_1} is too close, ignoring download ...')
return pd.DataFrame()
cur_dt = pd.Timestamp(dt).strftime('%Y-%m-%d')
asset = ticker.split()[-1]
info_log = f'{ticker} / {cur_dt} / {typ}'
if asset in ['Equity', 'Curncy', 'Index', 'Comdty']:
exch = const.exch_info(ticker=ticker)
if exch.empty: return pd.DataFrame()
else:
logger.error(f'unknown asset type: {asset}')
return pd.DataFrame()
time_fmt = '%Y-%m-%dT%H:%M:%S'
time_idx = pd.DatetimeIndex([
f'{cur_dt} {exch.allday[0]}', f'{cur_dt} {exch.allday[-1]}']
).tz_localize(exch.tz).tz_convert(DEFAULT_TZ).tz_convert('UTC')
if time_idx[0] > time_idx[1]: time_idx -= pd.TimedeltaIndex(['1D', '0D'])
q_tckr = ticker
if exch.get('is_fut', False):
if 'freq' not in exch:
logger.error(f'[freq] missing in info for {info_log} ...')
is_sprd = exch.get('has_sprd', False) and (len(ticker[:-1]) != exch['tickers'][0])
if not is_sprd:
q_tckr = fut_ticker(gen_ticker=ticker, dt=dt, freq=exch['freq'])
if q_tckr == '':
logger.error(f'cannot find futures ticker for {ticker} ...')
return pd.DataFrame()
info_log = f'{q_tckr} / {cur_dt} / {typ}'
miss_kw = dict(ticker=ticker, dt=dt, typ=typ, func='bdib')
cur_miss = missing.current_missing(**miss_kw)
if cur_miss >= 2:
if batch: return pd.DataFrame()
logger.info(f'{cur_miss} trials with no data {info_log}')
return pd.DataFrame()
logger.info(f'loading data from Bloomberg: {info_log} ...')
con, _ = create_connection()
data = con.bdib(
ticker=q_tckr, event_type=typ, interval=1,
start_datetime=time_idx[0].strftime(time_fmt),
end_datetime=time_idx[1].strftime(time_fmt),
)
if not isinstance(data, pd.DataFrame):
raise ValueError(f'unknown output format: {type(data)}')
if data.empty:
logger.warning(f'no data for {info_log} ...')
missing.update_missing(**miss_kw)
return pd.DataFrame()
data = data.tz_localize('UTC').tz_convert(exch.tz)
storage.save_intraday(data=data, ticker=ticker, dt=dt, typ=typ)
return pd.DataFrame() if batch else assist.format_intraday(data=data, ticker=ticker)
def bdh(tickers, flds, start_date, end_date='today', adjust=None, **kwargs):
"""
Bloomberg historical data
Args:
tickers: ticker(s)
flds: field(s)
start_date: start date
end_date: end date - default today
adjust: `all`, `dvd`, `normal`, `abn` (=abnormal), `split`, `-` or None
exact match of above words will adjust for corresponding events
Case 0: `-` no adjustment for dividend or split
Case 1: `dvd` or `normal|abn` will adjust for all dividends except splits
Case 2: `adjust` will adjust for splits and ignore all dividends
Case 3: `all` == `dvd|split` == adjust for all
Case 4: None == Bloomberg default OR use kwargs
**kwargs: overrides
Returns:
pd.DataFrame
Examples:
>>> bdh(
... tickers='VIX Index', flds=['High', 'Low', 'Last_Price'],
... start_date='2018-02-05', end_date='2018-02-07',
... ).round(2).transpose().rename_axis((None, None))
2018-02-05 2018-02-06 2018-02-07
VIX Index High 38.80 50.30 31.64
Low 16.80 22.42 21.17
Last_Price 37.32 29.98 27.73
>>> bdh(
... tickers='AAPL US Equity', flds='Px_Last',
... start_date='20140605', end_date='20140610', adjust='-'
... ).round(2)
ticker AAPL US Equity
field Px_Last
2014-06-05 647.35
2014-06-06 645.57
2014-06-09 93.70
2014-06-10 94.25
>>> bdh(
... tickers='AAPL US Equity', flds='Px_Last',
... start_date='20140606', end_date='20140609',
... CshAdjNormal=False, CshAdjAbnormal=False, CapChg=False,
... ).round(2)
ticker AAPL US Equity
field Px_Last
2014-06-06 645.57
2014-06-09 93.70
"""
logger = logs.get_logger(bdh, level=kwargs.pop('log', logs.LOG_LEVEL))
# Dividend adjustments
if isinstance(adjust, str) and adjust:
if adjust == 'all':
kwargs['CshAdjNormal'] = True
kwargs['CshAdjAbnormal'] = True
kwargs['CapChg'] = True
else:
kwargs['CshAdjNormal'] = 'normal' in adjust or 'dvd' in adjust
kwargs['CshAdjAbnormal'] = 'abn' in adjust or 'dvd' in adjust
kwargs['CapChg'] = 'split' in adjust
con, _ = create_connection()
elms = assist.proc_elms(**kwargs)
ovrds = assist.proc_ovrds(**kwargs)
if isinstance(tickers, str): tickers = [tickers]
if isinstance(flds, str): flds = [flds]
s_dt = utils.fmt_dt(start_date, fmt='%Y%m%d')
e_dt = utils.fmt_dt(end_date, fmt='%Y%m%d')
logger.info(
f'loading historical data from Bloomberg:\n'
f'{assist.info_qry(tickers=tickers, flds=flds)}'
)
return con.bdh(
tickers=tickers, flds=flds, elms=elms, ovrds=ovrds,
start_date=s_dt, end_date=e_dt,
).rename_axis(None)
import pytest
from xbbg import const
from xbbg.io import files, logs, storage
from xbbg.core import utils, assist
from xbbg.core.timezone import DEFAULT_TZ
from xbbg.core.conn import with_bloomberg, create_connection
if hasattr(pytest, 'config'):
if 'with_bbg' not in pytest.config.option:
pytest.skip('no Bloomberg')
if not pytest.config.option.with_bbg:
pytest.skip('no Bloomberg')
if hasattr(sys, 'pytest_call'): create_connection()
@with_bloomberg
def bdp(tickers, flds, cache=False, **kwargs):
"""
Get reference data and save to
Args:
tickers: tickers
flds: fields to query
cache: bool - use cache to store data
**kwargs: overrides
Returns:
pd.DataFrame